Generalized URV subspace tracking LMS algorithm

S. Hosur, A. H. Tewfik, D. Boley

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations

Abstract

The convergence rate of the Least Mean Squares (LMS) algorithm is poor whenever the adaptive filter input auto-correlation matrix is ill-conditioned. In this paper we propose a new LMS algorithm to alleviate this problem. It uses a data dependent signal transformation. The algorithm tracks the subspaces corresponding to clusters of eigenvalues of the auto-correlation matrix of the input to the adaptive filter, which have the same order of magnitude. The algorithm up-dates the projection of the tap weights of the adaptive filter onto each subspace using LMS algorithms with different step sizes. The technique also permits adaptation only in those subspaces, which contain strong signal components leading to a lower excess Mean Squared Error (MSE) as compared to traditional algorithms.

Original languageEnglish (US)
Article number390003
Pages (from-to)III409-III412
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume3
DOIs
StatePublished - 1994
EventProceedings of the 1994 IEEE International Conference on Acoustics, Speech and Signal Processing. Part 2 (of 6) - Adelaide, Aust
Duration: Apr 19 1994Apr 22 1994

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